Field of the Invention
The invention relates generally to collecting and processing satellite imagery aggregated with large volumes of other digitized data for analysis by a human user in order to identify geographic areas for further data collection and/or analysis. More particularly, the invention relates to identifying geographic subjects for satellite image acquisition by aggregating and analyzing first-time changes detected by satellite imagery as well as weather report data, social media streams and newswire feeds. The data is filtered to identify potentially relevant data by use of rules, then cataloged and stored in a data warehouse where it may be queried or used to prepare a report for a user useful for directing satellite image acquisition.
Description of Related Art
Commercial and governmental enterprises may be interested in collecting, aggregating, and analyzing the enormous amounts of information available from multiple sources. Governmental intelligence agencies especially may desire information on certain regions of interest. These sources may be proprietary to the enterprise, public, or quasi-public. For example, historical and substantially contemporaneous satellite imagery is available, weather data is widely available, and as the internet has grown an enormous amount of information is available from companies which participate in the “social media” arena. The growth of the internet, and the millions of personal devices connected to it, make it virtually impossible for any person to aggregate and analyze the torrent of information generated daily. This problem may be especially acute for national intelligence agencies, which have limited resources available to harvest, compile, and distill data, including the vast quantity of third party data on social media (e.g. Facebook, Twitter, Instagram, etc.), and may desire to promptly redirect intelligence collection and/or analysis, more particularly redirecting satellite image acquisition targets to areas where new images are most likely to yield useful information.
Enterprises currently have available to them some helpful tools. For example, MDA Information Systems, LLC has developed systems that use satellite imagery to identify and track geophysical developments, known as the MDA “Refined Persistent-change Model” (RPM), substantial portions of which are disclosed in U.S. Pat. No. 8,548,248 “Correlated Land Change System and Method” (CLC), which disclosure is hereby incorporated in its entirety. More particularly, that CLC patent discloses a system and method for identifying and confirming a “persistent feature change” based on a plurality of satellite images. Identifying confirmed persistent feature changes, particularly first time changes, are an invaluable source of intelligence. This system is available commercially from MDA Information Systems, LLC as “Persistent Change Monitoring” marketed as PCM®.
Another source of information is weather conditions. Weather conditions may be compared with new remotely detected alterations on the earth's surface (identified by satellite, for example) to help determine whether a change feature is the result of a natural weather event, such as snow, flood or drought. Data relating to weather information is available from a variety of government and private sources, including from MDA Information Systems, LLC through its Weather Desk platform.
A large source of information is asynchronous data that may be open source or available for purchase. “Open source” data is data that is available for acquisition at no or minimal charge from public or private sources, such as the geographical mapping program Open Street Maps (www.openstreetmap.org). “Asynchronous data” commonly means data or a data process that is generated or operates independently of controlled processes. Examples of asynchronous data include newswire feeds, social media visible including metadata (e.g. Twitter, Facebook, Instagram, YouTube), text messages, Open Street Maps data, weather data, and earthquake data.
The problems with analyzing asynchronous data—especially social media—are its volume, variety, velocity, veracity. It is possible to hire and train analysts to extract essential information from open sources, but it is expensive, and the ever-growing volume of traffic precludes analysis and distillation of all but a tiny fraction of available information.
Some tools to monitor social media have also been introduced, such as the MDA products Open and All Source Intelligence Service (OASIS) and Geotagged Open Street Search Intelligence Profiler (GOSSIP). These products harvest geo-tagged social media from U.S. and international sources relating to thousands of features people, places, events, and equipment. But users must manually correlate information harvested from social media with other data sources, such as satellite imagery, news stories, and weather reports. The volume and scope of the required manual analysis effectively precludes timely aggregation and processing of changing imagery from satellites news and weather reports, on-ground photographs, and social media activity. Such information has never been harvested, processed, and aggregated using geospatial and temporal criteria automatically, in near-real-time, using criteria that the system or a user can automatically modify in response to near-real-time observed activity.
What is needed is a system and method to improve analyst and intelligence asset efficiency through harvesting, compiling, and distilling open source data according to self-generated and/or user-specified criteria to qualify the data that is most likely to be relevant to directing further intelligence collection and analysis, and more particularly useful for directing satellites to acquire images of geographic areas having higher probability of yielding useful intelligence information. The system would harvest a vast amount of open source data, seek out anomalies or changes, review and correlate developments and anomalies, compile the essential information, and provide analysts with tipoffs/cues for further investigations. By pre-processing aggregated data, the system will allow more in-depth analysis, increase areal coverage, and incorporate relevant, essential information from the explosion of new open source data. The objective would be for the system to provide only the essential, qualified open source-derived information to the enterprise as analyst-ready information with geospatial content, facilitating the direction of intelligence assets and more particularly gathering valuable satellite imagery.